added SD for examples

.github/workflows/test_openvino_examples.yml

@@ -1,20 +1,16 @@
-name: OpenVINO - Notebooks and Examples Test
+name: OpenVINO - Examples Test
 
 on:
   workflow_dispatch:
   schedule:
     - cron:  '14 3 * * 1'  # run weekly: every Monday at 3:14
   push:
     paths:
-    - '.github/workflows/test_openvino_notebooks.yml'
     - '.github/workflows/test_openvino_examples.yml'
-    - 'notebooks/openvino/*'
     - 'examples/openvino/*'
   pull_request:
     paths:
-    - '.github/workflows/test_openvino_notebooks.yml'
     - '.github/workflows/test_openvino_examples.yml'
-    - 'notebooks/openvino/*'
     - 'examples/openvino/*'
 
 
@@ -42,11 +38,8 @@ jobs:
       run: |
         # Install PyTorch CPU to prevent unnecessary downloading/installing of CUDA packages
         # ffmpeg, torchaudio and pillow are required for image classification and audio classification pipelines
-        sudo apt-get install ffmpeg
-        # pip install torch torchaudio --extra-index-url https://download.pytorch.org/whl/cpu
-        pip install ".[tests, openvino]" nbval
-        pip install optimum[openvino] nncf torchaudio datasets==2.4.0 
-        pip install -r notebooks/openvino/requirements.txt
+        pip install optimum[openvino] nncf
+        pip install -r examples/openvino/audio-classification/requirements.txt
         pip install -r examples/openvino/image-classification/requirements.txt
         pip install -r examples/openvino/question-answering/requirements.txt
         pip install -r examples/openvino/text-classification/requirements.txt
@@ -55,13 +48,10 @@ jobs:
     - run: lscpu
     - run: pip freeze
 
-    - name: Test with Pytest
-      run: |
-        python -m pytest --nbval-lax notebooks/openvino/optimum_openvino_inference.ipynb  notebooks/openvino/question_answering_quantization.ipynb
-
     - name: Test examples
       run: |
-        #python examples/openvino/audio-classification/run_audio_classification.py     --model_name_or_path facebook/wav2vec2-base     --nncf_compression_config examples/openvino/audio-classification/configs/wav2vec2-base-qat.json     --dataset_name superb     --dataset_config_name ks     --max_train_samples 10   --max_eval_samples 2  --output_dir /tmp/qat-wav2vec2-base-ft-keyword-spotting     --overwrite_output_dir     --remove_unused_columns False     --do_train   --learning_rate 3e-5     --max_length_seconds 1     --attention_mask False     --warmup_ratio 0.1     --num_train_epochs 1    --gradient_accumulation_steps 1    --dataloader_num_workers 1     --logging_strategy steps     --logging_steps 1     --evaluation_strategy epoch     --save_strategy epoch     --load_best_model_at_end False    --seed 42
+        python examples/openvino/audio-classification/run_audio_classification.py  --model_name_or_path facebook/wav2vec2-base  --nncf_compression_config examples/openvino/audio-classification/configs/wav2vec2-base-qat.json  --dataset_name superb  --dataset_config_name ks  --max_train_samples 10  --max_eval_samples 2  --output_dir /tmp/qat-wav2vec2-base-ft-keyword-spotting  --overwrite_output_dir  --remove_unused_columns False  --do_train  --learning_rate 3e-5  --max_length_seconds 1  --attention_mask False     --warmup_ratio 0.1     --num_train_epochs 1    --gradient_accumulation_steps 1    --dataloader_num_workers 1     --logging_strategy steps     --logging_steps 1     --evaluation_strategy epoch     --save_strategy epoch     --load_best_model_at_end False    --seed 42
         TASK_NAME=sst2  &&  python examples/openvino/text-classification/run_glue.py     --model_name_or_path sshleifer/tiny-distilbert-base-cased-distilled-squad     --task_name $TASK_NAME    --max_train_samples 10   --max_eval_samples 2   --output_dir /tmp/qat-bert-base-ft-$TASK_NAME     --overwrite_output_dir     --do_train     --do_eval     --max_seq_length 128     --learning_rate 1e-5     --optim adamw_torch     --num_train_epochs 1     --logging_steps 10     --evaluation_strategy steps     --eval_steps 5     --save_strategy epoch     --seed 42
         python examples/openvino/question-answering/run_qa.py   --model_name_or_path sshleifer/tiny-distilbert-base-cased-distilled-squad   --dataset_name squad   --do_train   --do_eval     --max_train_samples 10   --max_eval_samples 2   --learning_rate 3e-5   --num_train_epochs 1   --max_seq_length 384   --doc_stride 128   --output_dir /tmp/outputs_squad/   --overwrite_output_dir
         python examples/openvino/image-classification/run_image_classification.py     --model_name_or_path nateraw/vit-base-beans     --dataset_name beans     --max_train_samples 10   --max_eval_samples 2  --remove_unused_columns False     --do_train  --learning_rate 2e-5     --num_train_epochs 1    --logging_strategy steps     --logging_steps 1     --evaluation_strategy epoch     --save_strategy epoch     --save_total_limit 1     --seed 1337     --output_dir /tmp/beans_outputs/
+        pip install -r examples/openvino/stable-diffusion/requirements.txt && python examples/openvino/stable-diffusion/train_text_to_image_qat.py --ema_device="cpu"    --use_kd  --model_id="svjack/Stable-Diffusion-Pokemon-en"   --max_train_samples 10  --center_crop  --random_flip   --dataloader_num_workers=2   --dataset_name="lambdalabs/pokemon-blip-captions"   --max_train_steps=1 --output_dir=sd-quantized-pokemon